Asymmetrical glymphatic dysfunction in patients with Long Covid associated neurocognitive impairment- Correlation with BBB disruption

Asymmetrical glymphatic dysfunction in patients with Long Covid associated neurocognitive impairment- Correlation with BBB disruption | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Asymmetrical glymphatic dysfunction in patients with Long Covid associated neurocognitive impairment- Correlation with BBB disruption Joga R Chaganti, Tanush K Talekar, Bruce James Brew This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4551571/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2025 Read the published version in BMC Neurology → Version 1 posted 10 You are reading this latest preprint version Abstract Background and Purpose The glymphatic system, a waste clearance pathway, has been implicated in several neurological conditions associated with neuroinflammation. COVID-19 associated neurocognitive impairment, part of the post-acute sequelae of SARS-CoV-2 infection (PASC), is strongly associated with neuroinflammation and disrupted blood-brain barrier (BBB). Several studies have implicated a synergistic interaction between the glymphatic system dysfunction and BBB disruption. In this proof-of-concept study, we investigated the relationship of the diffusion along the perivascular spaces DTI (DTI-ALPS) and increased capillary permeability metric- K trans derived from DCE perfusion in patients with PASC. Materials and Methods 14 subjects with PASC who had persisting symptoms of anosmia, ageusia, fatigue, and cognitive impairment (CI) and ten healthy age and sex matched controls were recruited. All PASC subjects underwent routine and advanced MR imaging early at two time points, (3 months +/- 2 weeks) referred as Time Point 1 (TP-1) and 10 repeated the MRI scan 12 months (+/- 2 weeks) after referred as Time Point 2 (TP-2), while the controls had MR imaging done only at TP-1. All had elaborate neurocognitive assessment. In the final analysis we included those who had DTI study at both time points (n-10). MR imaging included DCE perfusion and DTI in addition to anatomical imaging. Statistical analysis Given the small size of the sample and nonnormality of data in the descriptive analyses, nonparametric analyses were used for group comparisons. A two-sample Wilcoxon rank sum test was used to show the differences in DTI-ALPS between the patients and controls in the predefined ROI. Spearman’s correlation coefficient (rho) was used to assess the correlation between K-trans and DTI-ALPS index. Results There was significant reduction in DTI-ALPS index between the patients and controls in the left hemisphere (z=2.04, p < 0.04). However, there was no significant change over time in the index. There was a strong inverse correlation between the central white matter K trans and DTI-ALPS index (rho=0.66, p< 0.03). Conclusion Our study suggests that BBB disruption and disordered glymphatic drainage may contribute to neuroaxonal injury in patients with PASC, and DTI-ALPS index could serve as a powerful non-invasive biomarker. Figures Figure 1 Figure 2 INTRODUCTION Existing evidence indicates neuroinflammation is one of the important driving forces that is responsible for neurocognitive impairment in patients with PASC. Several studies have reported that PASC is associated with a disrupted blood-brain barrier, an indication of neuroinflammation, and imaging evidence of increased capillary permeability ( 1 ) . These observations are similar in patients with several neurocognitive disorders such as Alzheimer’s disease where increased permeability is also associated with decreased clearance of solutes ( 2 ) . New insights indicate that this solute clearance is a function of glymphatic system ( 3 ) . The glymphatic system an alternate pathway of the scavenger system in the brain and facilitates the exchange of the metabolites between the perivascular space CSF and the brain. This is a brain-wide pathway for fluid transport, and possibly starts as para-arterial influx of subarachnoid CSF into the brain interstitium, followed by the clearance of interstitial fluid (ISF) along large-calibre draining veins as well as through the basal foramina and from there on to cervical lymphatics ( 4 ) . The glymphatic system has been investigated in vivo using dynamic contrast-enhanced MRI, intrathecal administration of gadolinium and dynamic 11C-Pittsburgh Compound B positron emission tomography techniques. Recently, diffusion MRI has been proposed as a non-invasive method to quantify glymphatic function by calculating the diffusion tensor image metrics along the perivascular space (DTI-ALPS) index ( 5 ) . The DTI-ALPS index appears to be correlated with classical detection methods of glymphatic clearance function. This method has been applied in studies on Alzheimer’s disease, Parkinson’s disease, ischemic stroke, sleep, idiopathic normal pressure hydrocephalus, tumor-associated cerebral edema and idiopathic intracranial hypertension ( 6 – 10 ) . In this “proof-of-concept” study, we investigated the relationship of the diffusion along the perivascular spaces derived from DTI (DTI-ALPS) and the capillary permeability metric k trans in patients with PASC. We assumed that increased capillary permeability may be associated with reduced clearance of the solutes from the interstitial space, to explain the complex mechanistic implication in PASC -NCI. Materials And Methods 14 subjects with PASC who had persisting symptoms of anosmia, ageusia, fatigue, and NCI who were clinically evaluated at the Neurology Department at SVH between July 2021-August − 2022 were consented for the use of their clinical data for research. All 14 patients have been referred for CI post COVID and all underwent a full neurological assessment (BJB). All the subjects were right-handed. MR imaging was conducted as part of the neurological assessment. All participants underwent routine and advanced MR imaging early in the disorder (3 months +/- 2 weeks) referred as Time Point 1 (TP-1) and 10 repeated the MRI scan 12 months (+/- 2 weeks) after referred as Time Point 2 (TP-2). Hence in the final analysis we included those who had DTI study at both time points (n-10). Seven of those participants has been enrolled into the ADAPT study, a prospective cohort of 128 SARS-CoV-2 positive patients and had received serial measurements of cognition with the Cogstate Brief Battery ( 11 ) . Individuals with a prior history of drug use, significant head injury, psychiatric illness, and hepatitis C virus co-infection were excluded. Ten healthy age and sex matched controls were recruited and underwent the same neurological assessments at one time point (BJB) (Table-1). Local ethics approval was obtained. (2022/ETH0022). Table-1 : Baseline characteristics among PASC CI cases and controls Cases (n = 10) Controls (n = 10) Sex Male 4 (43%) Sd: 2.24 5 (50%) Female 6 (57%) Sd: 1.97 5 (50%) Age (years, mean and SD) 49 (+/-2) 46 (+/-1.7) Duration between COVID-19 diagnosis and first MRI (weeks, mean and SD) 12 (+/- 1) N/A Acute Covid Severity Mild 6 (78.9%) Moderate 3 (14.2%) Severe 1 (7.1%) Neurological Symptoms N/A Loss of Smell 9 (90%) Loss of Taste 8 (80%) Cognitive difficulty 10 (100%) Tiredness/Myalgias 10 (100%) Neurology at TP-1 N/A Loss of Smell 10 (100%) Loss of Taste 10 (100%) Tiredness 10 (100%) Cognitive difficulty 10 (100%) Imaging was performed with a 3TMR imaging scanner (Ingenia; Philips Healthcare, Best, the Netherlands) with a 24-channel head coil. DCE perfusion imaging, 32 directional diffusion imaging and single voxel in addition to the routine clinical imaging (T-1 volumetric imaging and T-2 FSE) were performed. T1-weighted imaging was performed with the following parameters 3DT-1 spoiled gradient recalled acquisition in steady state (SPGR): 128 sagittal slices, 1mm isotropic, time to repeat/time to echo (TR/ TE): shortest, field of view: 240, Matrix: 256/256. Diffusion tensor imaging (DTI) : The DTI protocol consisted of a single-shot spin-echo-based echo- planar diffusion-weighted imaging with three averages and 36 gradient encoding directions, with b values of 0 and 1,000 s/mm2. The imaging parameters were slice thickness 5mm, interslice gap 1.5mm, FOV: 230 X230, matrix 128/128, TR 3500 and TE 96Msec. Image processing. DCE perfusion MRI The DCE-MRI sequence was obtained using 3D T1- weighted spoiled gradient echo sequence in the axial plane covering the entire brain [TR and TE1⁄4shortest (Act TR/TE 15/3.0 ms, temporal resolution 5.8, flip angle 150, matrix = 184 x 141, number of slices = 23, slice thickness = 4 mm, number of signal averages = 1, temporal resolution = 5.8/dynamic, number of dynamics = 90 and scanning time = 9.06 min. Contrast injection was commenced 6 s after the start of the dynamic MRI acquisitions, given in the form of a bolus injection of gadobutrol (Gadovist, Bayer, California, USA) at a concentration of 0.1 mmol/kg of body weight at 3 ml/s. Following the DCE-MRI scan, postcontrast- enhanced volumetric T1-weighted images were acquired as part of the routine clinical examination. Image Processing: DTI and DTI-ALPS: The DTI was processed using FSL toolbox ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki ). FSL's eddy correction tool was used for pre-processing the data. The brain extraction tool (BET) was used to create brain masks. Subsequently, water diffusivity along the x (Dx), y(Dy), and z (Dy) axes and fractional anisotropy (FA) maps were computed for each DTI scan. The tract-based skeleton statistic (TBSS) was used for the registration of FA maps from each participant onto an MNI FA atlas. All diffusivity maps were aligned to the same space using TBSS non-FA scripts. This method extracted affine matrices and warp fields derived from FA registration and applied them to these diffusivity maps. To calculate the DTI-ALPS, regions of interest (ROI) were defined as 3 mm x 3mm rectangle ROIs. The ROI were placed in the projection- and association-fiber regions in the horizontal plane of the lateral ventricle body. Three ROIs were placed in bilateral regions (Fig. 1 a). The DTI-ALPS is calculated as: DTI-ALPS= (mean (Dx (proj),Dy (assoc)))/(mean (Dy(proj),Dz (assoc)) ) Here, Dx (proj) and Dy (assoc), Dz (assoc) are the mean diffusivity in the ROI placed in projection fibers and the association fibers along the x-axis, y-axis, and z-axis, respectively. The DTI-ALPS values were calculated for each patient and controls at baseline and patients at the longitudinal timepoint. DCE perfusion MRI DCE studies were processed with nordicICE [nordicICE (NICE) 4.0.4; NordicNeuroLab, Bergen, Norway], a propriety software that includes brain extraction, motion correction and image registration. We assessed the DCE-derived metric K trans in multiple regions of the brain. The k trans images were interrogated by placing multiple regions of interest (ROI) in the following areas of the brain [basal ganglia (caudate and lentiform nucleus), frontal cortex, frontal white matter, thalami, splenium of corpus callosum, occipital cortex and white matter, internal capsule, brainstem and cerebellar lobes) by two radiologists, one with 25 years of experience and one with 3 year of experience (JC, AT) (Fig. 1 ). The volumes of the ROIs were 0.7ml and whenever the area was smaller due to volume loss, the ROI was adjusted to reduce the effects of CSF. K trans values were obtained from identical regions of the brain from the opposite hemispheres and the average values were taken to compare with the normal controls (e.g. average values of both hemispheres interrogated from individual anatomical regions). Statistical Analysis Out of 14 patients, only 10 patients had follow-up DTI study and hence in the final analysis we included those who had DTI study at both time points (n-10). Given the small size of the sample and nonnormality of data in the descriptive analyses, nonparametric analyses were used for group comparisons. A two-sample Wilcoxon rank sum test was used to show the differences in DTI-ALPS between the patients and controls in the predefined ROIS. Paired Wilcoxon rank sum test was used to compare the DTI-ALPS values between two time points and within the regions of interest (Both hemispheres). Spearman’s correlation coefficient (rho) was used to assess the correlation between blood brain barrier measure (K-trans) and DTI-ALPS scores in the regions where there was significant difference between group. Results DTI-ALPS: There was a significant difference (z = 2.04, p = 0.04) in DTI-ALPS between patients and controls in the left hemisphere (Fig. 1 a, 1 b). However, there was no statistically significant change (p > 0.05) over time within patients in this ROI. There was a significant difference in DTI-ALPS between Left and Right ROI within patients (z = 2.5, p = 0.01) Correlation with K trans and Neurocognitive scores: Exploratory correlations were performed between K-Trans, ALPS index and neurocognitive scores. There was no correlation of NCS with ALPS score. Mean K trans scores did not correlate with ALPS score. However central white matter K trans has shown positive correlation on the side of abnormal ALPS (rho = 0.66, p < 0.03) (Fig. 2). Discussion In this proof-of-concept prospective case-control study, we investigated glymphatic system abnormalities, as expressed by the DTI-ALPS index, and correlated them with capillary permeability and neurocognitive scores in individuals affected by neurocognitive impairment due to COVID-19. Our findings revealed decrease in the DTI-ALPS index in the left hemisphere, while the ALPS index in the right hemisphere remained within normal limits. We observed a strong negative correlation between the DTI-ALPS index and K trans in the central white matter, but no statistically significant correlation was found between the ALPS index and neurocognitive scores. We believe our study is the first to delve into the relationship between DTI-ALPS and the BBB metric K trans. Our own prior research has revealed disruptions in the BBB, likely triggered by glutamatergic excitotoxicity, and subsequent changes in white matter integrity among patients with long COVID ( 1 ) . The BBB disruption, a hallmark of neuroinflammation, is a known characteristic of several neurodegenerative disorders, often occurring alongside abnormalities in the glymphatic system, a crucial paravascular drainage pathway in the brain. Wu et al in their study has shown variable ALPS changes in long COVID-19 subjects and concluded that it is likely secondary to neuronal inflammation and secondary glymphatic dysfunction ( 12 ) . The glymphatic system functions to expel metabolic waste from the brain's interstitium via paravascular spaces, either into the dural sinuses or through perineural spaces from the basal foramen into the cervical lymphatics during REM sleep. Anosmia, a principal presentation of COVID-19, and the volume loss in the brain regions associated with smell, and memory indicate that the influx and efflux mechanisms in the perineural space of the olfactory bulbs is damaged and a resultant abnormalities in glymphatic drainage from the olfactory-gustatory circuit ( 4 , 12 ) . Several earlier studies have identified excitotoxicity as a primary pathological mechanism contributing to neurocognitive impairment in long COVID-19 ( 13 ) . The excitotoxicity, in addition to causing blood-brain barrier (BBB) disruption, also is known to impair aquaporin-4 channels ( 1 , 14 ) . This dysregulation of water channels has been shown to result in impaired cerebrospinal fluid (CSF) influx and disruption of the CSF-interstitial fluid (ISF) turnover pathway ( 15 ) and thus appear to be having a synergistic relation with BBB disruption. Diffusion tensor imaging metrics from projection and association fibers at the mid-body level of lateral ventricles, where medullary veins run perpendicular to ventricular bodies, have been used to measure paravascular flow in the interstitium. This metric, expressed as the ALPS Index, is a non-invasive method to measure glymphatic drainage abnormalities and has been shown to be abnormal in several neurological disorders associated with neuroinflammation. Our study revealed an asymmetric reduction in the ALPS index in the left hemisphere compared to the right which is not surprising given that most studies in the literature measure the ALPS index from the dominant hemisphere, where diffusion metrics differ from the non-dominant hemisphere ( 16 ) . Wu et al study has shown similar observations with asymmetrical glymphatic dysfunction between hemispheres ( 12 ) . Furthermore, this heterogeneity in the involvement could be secondary to non-uniform neuroinflammation. Our earlier longitudinal study in patients with long COVID also showed asymmetrical diffusion changes between hemispheres, indicating variable neuroaxonal injury across the brain ( 1 ) . Additionally, we also found that Dx- values in COVID-19 were mildly decreased compared to controls, leading to a decrease in the DTI-ALPS index (trending significance with variance between the controls and patients is 30.3% on polynomial regression). This suggests that diffusion is hindered more significantly in projection fibers than in association fibers, particularly in the plane perpendicular to the ventricles, possibly due to increased extracellular water content and changes in white matter fiber structure. The lack of significant change in the ALPS index over 12 months suggests that alterations contributing to the pathogenesis of altered drainage may take longer to revert or may be irreversible, a finding also reflected in the lack of statistically significant change in K trans values. Despite the valuable insights provided by our study, several limitations exist, primarily the small sample size, partially compensated by the longitudinal design. Additionally, not all subjects underwent a full battery of neurocognitive assessments. Future studies with larger sample sizes and longitudinal designs are warranted to further elucidate these findings. Conclusion Our study suggests that neuroinflammation and disordered drainage may contribute to neuroaxonal injury in long COVID-19, and DTI-ALPS could serve as a powerful non-invasive biomarker to identify these abnormalities. Abbreviations PASC: Post-acute sequelae of SARS-CoV-2 infection, DTI-ALPS: Diffusion along the perivascular spaces DTI (DTI-ALPS), NCI: Neuro cognitive impairment, BBB: Blood brain barrier. Declarations Ethics approval and consent to participate: Local ethics approval was obtained. (2022/ETH0022). Consent for publication: Not Applicable. Availability of data and materials : The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: Authors did not receive any funding for this research. Acknowledgements: We have obtained part of the cohort of patient’s data from the ADAPT Study (PI: GM: [email protected] ). Authors' contributions: Joga Chaganti : Concept and Major contributor in writing article and processing of the DTI data. Tanush Talekar : DTI evaluation and processing. Bruce Brew : Concept and Major contributor in writing the article and neurocognitive testing. References Chaganti J, Poudel G, Cysique LA, Dore GJ, Kelleher A, Matthews G, et al. Blood brain barrier disruption and glutamatergic excitotoxicity in post-acute sequelae of SARS COV-2 infection cognitive impairment: potential biomarkers and a window into pathogenesis. Frontiers in Neurology. 2024;15:1350848. Liang T, Chang F, Huang Z, Peng D, Zhou X, Liu W. Evaluation of glymphatic system activity by diffusion tensor image analysis along the perivascular space (DTI-ALPS) in dementia patients. The British journal of radiology. 2023;96(1146):20220315. Taoka T, Masutani Y, Kawai H, Nakane T, Matsuoka K, Yasuno F, et al. Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer’s disease cases. Japanese journal of radiology. 2017;35:172-8. Albayram MS, Smith G, Tufan F, Tuna IS, Bostancıklıoğlu M, Zile M, et al. Non-invasive MR imaging of human brain lymphatic networks with connections to cervical lymph nodes. Nature communications. 2022;13(1):203. Thakkar RN, Kioutchoukova IP, Griffin I, Foster DT, Sharma P, Valero EM, et al. Mapping the Glymphatic pathway using imaging advances. J. 2023;6(3):477-91. Steinruecke M, Tiefenbach J, Park JJ, Kaliaperumal C. Role of the glymphatic system in idiopathic intracranial hypertension. Clinical Neurology and Neurosurgery. 2022;222:107446. Ringstad G, Vatnehol SAS, Eide PK. Glymphatic MRI in idiopathic normal pressure hydrocephalus. Brain. 2017;140(10):2691-705. Yang G, Deng N, Liu Y, Gu Y, Yao X. Evaluation of glymphatic system using diffusion MR technique in T2DM cases. Frontiers in human neuroscience. 2020;14:300. Kamagata K, Andica C, Takabayashi K, Saito Y, Taoka T, Nozaki H, et al. Association of MRI indices of glymphatic system with amyloid deposition and cognition in mild cognitive impairment and Alzheimer disease. Neurology. 2022;99(24):e2648-e60. Wu CH, Chang FC, Wang YF, Lirng JF, Wu HM, Pan LLH, et al. Impaired glymphatic and meningeal lymphatic functions in patients with chronic migraine. Annals of Neurology. 2024. Cysique LA, Jakabek D, Bracken SG, Allen‐Davidian Y, Heng B, Chow S, et al. The kynurenine pathway relates to post‐acute COVID‐19 objective cognitive impairment and PASC. Annals of Clinical and Translational Neurology. 2023;10(8):1338-52. Wu L, Zhang Z, Liang X, Wang Y, Cao Y, Li M, et al. Glymphatic system dysfunction in recovered patients with mild COVID-19: A DTI-ALPS study. Iscience. 2024;27(1). Greene C, Connolly R, Brennan D, Laffan A, O’Keeffe E, Zaporojan L, et al. Blood–brain barrier disruption and sustained systemic inflammation in individuals with long COVID-associated cognitive impairment. Nature Neuroscience. 2024;27(3):421-32. Manganotti P, Michelutti M, Furlanis G, Deodato M, Stella AB. Deficient GABABergic and glutamatergic excitability in the motor cortex of patients with long-COVID and cognitive impairment. Clinical Neurophysiology. 2023;151:83-91. Iliff JJ, Wang M, Zeppenfeld DM, Venkataraman A, Plog BA, Liao Y, et al. Cerebral arterial pulsation drives paravascular CSF–interstitial fluid exchange in the murine brain. Journal of Neuroscience. 2013;33(46):18190-9. Taoka T, Naganawa S. Neurofluid dynamics and the glymphatic system: a neuroimaging perspective. Korean Journal of Radiology. 2020;21(11):1199. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2025 Read the published version in BMC Neurology → Version 1 posted Editorial decision: Revision requested 09 Sep, 2024 Reviews received at journal 06 Sep, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviews received at journal 05 Jul, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers invited by journal 16 Jun, 2024 Editor assigned by journal 12 Jun, 2024 Submission checks completed at journal 11 Jun, 2024 First submitted to journal 08 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4551571","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317363120,"identity":"a38d8859-b233-4afb-ac08-bc8c7fdb8920","order_by":0,"name":"Joga R Chaganti","email":"data:image/png;base64,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","orcid":"","institution":"Thomas Jefferson University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Joga","middleName":"R","lastName":"Chaganti","suffix":""},{"id":317363121,"identity":"d8870040-0328-4ab9-8f11-d4850baed7b9","order_by":1,"name":"Tanush K Talekar","email":"","orcid":"","institution":"Thomas Jefferson University","correspondingAuthor":false,"prefix":"","firstName":"Tanush","middleName":"K","lastName":"Talekar","suffix":""},{"id":317363129,"identity":"45726b94-cfe8-4cad-8246-f3da16e418b1","order_by":2,"name":"Bruce James Brew","email":"","orcid":"","institution":"University of New South Wales, University of Notre Dame Sydney, St Vincent’s Centre for Applied Medical Research, St Vincent's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bruce","middleName":"James","lastName":"Brew","suffix":""}],"badges":[],"createdAt":"2024-06-08 18:24:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4551571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4551571/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12883-025-04133-4","type":"published","date":"2025-03-19T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59504706,"identity":"da98f27a-0cb4-47e1-add4-e4860f6d59ca","added_by":"auto","created_at":"2024-07-02 14:52:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2746462,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA: ROI Localisation for DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ROIs localisation in the Projection and Association fibers. The right association fiber (MNI: 36, -23, 31) and projection fiber (27, -23, 31), and left association fiber (-36, -23, 31) and left projection fiber (-37, -23, 31) are shown on a MNI template.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB: Bar graph depicting the results of Wilcoxon rank sum test:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea) The ROIs IN the association fibers and projection fibers according to previous work. [MNI Coordinates: The right association fiber (MNI: 36, -23, 31) and projection fiber (27, -23, 31) and left association fiber (-36, -23, 31) and left projection fiber (-37, -23, 31)].\u003c/p\u003e\n\u003cp\u003eB) There was a significant difference between controls and patients and DTI-ALPS in the left hemisphere as well as between the right and left hemispheres.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4551571/v1/15b2fd444192a8747cda9b65.png"},{"id":59504705,"identity":"52a41ecf-9fbc-4f14-94d1-be4f28050d5f","added_by":"auto","created_at":"2024-07-02 14:52:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman’s correlation coefficient\u003c/strong\u003e: \u003cstrong\u003eCorrelation between the K trans and DTI-ALPS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003escatter plot between the DTI-ALPS on X axis and K trans of the central white matter on y axis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4551571/v1/59bd9d53e1f0af89bca51cec.png"},{"id":79120369,"identity":"35466773-0c9a-4ce2-bc31-8da13e911e35","added_by":"auto","created_at":"2025-03-24 16:03:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4515116,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4551571/v1/6600f071-7be5-4bbf-a3be-1b33cd1478f2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Asymmetrical glymphatic dysfunction in patients with Long Covid associated neurocognitive impairment- Correlation with BBB disruption","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eExisting evidence indicates neuroinflammation is one of the important driving forces that is responsible for neurocognitive impairment in patients with PASC. Several studies have reported that PASC is associated with a disrupted blood-brain barrier, an indication of neuroinflammation, and imaging evidence of increased capillary permeability\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e. These observations are similar in patients with several neurocognitive disorders such as Alzheimer\u0026rsquo;s disease where increased permeability is also associated with decreased clearance of solutes\u003csup\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/sup\u003e. New insights indicate that this solute clearance is a function of glymphatic system\u003csup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/sup\u003e. The glymphatic system an alternate pathway of the scavenger system in the brain and facilitates the exchange of the metabolites between the perivascular space CSF and the brain. This is a brain-wide pathway for fluid transport, and possibly starts as para-arterial influx of subarachnoid CSF into the brain interstitium, followed by the clearance of interstitial fluid (ISF) along large-calibre draining veins as well as through the basal foramina and from there on to cervical lymphatics\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/sup\u003e. The glymphatic system has been investigated in vivo using dynamic contrast-enhanced MRI, intrathecal administration of gadolinium and dynamic 11C-Pittsburgh Compound B positron emission tomography techniques. Recently, diffusion MRI has been proposed as a non-invasive method to quantify glymphatic function by calculating the diffusion tensor image metrics along the perivascular space (DTI-ALPS) index\u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/sup\u003e. The DTI-ALPS index appears to be correlated with classical detection methods of glymphatic clearance function. This method has been applied in studies on Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, ischemic stroke, sleep, idiopathic normal pressure hydrocephalus, tumor-associated cerebral edema and idiopathic intracranial hypertension \u003csup\u003e(\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this \u0026ldquo;proof-of-concept\u0026rdquo; study, we investigated the relationship of the diffusion along the perivascular spaces derived from DTI (DTI-ALPS) and the capillary permeability metric k trans in patients with PASC. We assumed that increased capillary permeability may be associated with reduced clearance of the solutes from the interstitial space, to explain the complex mechanistic implication in PASC -NCI.\u003c/p\u003e"},{"header":"Materials And Methods","content":" \u003cp\u003e14 subjects with PASC who had persisting symptoms of anosmia, ageusia, fatigue, and NCI who were clinically evaluated at the Neurology Department at SVH between July 2021-August \u0026minus;\u0026thinsp;2022 were consented for the use of their clinical data for research. All 14 patients have been referred for CI post COVID and all underwent a full neurological assessment (BJB). All the subjects were right-handed. MR imaging was conducted as part of the neurological assessment. All participants underwent routine and advanced MR imaging early in the disorder (3 months +/- 2 weeks) referred as Time Point 1 (TP-1) and 10 repeated the MRI scan 12 months (+/- 2 weeks) after referred as Time Point 2 (TP-2). Hence in the final analysis we included those who had DTI study at both time points (n-10). Seven of those participants has been enrolled into the ADAPT study, a prospective cohort of 128 SARS-CoV-2 positive patients and had received serial measurements of cognition with the Cogstate Brief Battery \u003csup\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e. Individuals with a prior history of drug use, significant head injury, psychiatric illness, and hepatitis C virus co-infection were excluded. Ten healthy age and sex matched controls were recruited and underwent the same neurological assessments at one time point (BJB) (Table-1). \u003cb\u003eLocal ethics approval was obtained. (2022/ETH0022).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-1\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBaseline characteristics among PASC CI cases and controls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (43%) Sd: 2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (57%) Sd: 1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years, mean and SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (+/-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (+/-1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration between COVID-19 diagnosis and first MRI (weeks, mean and SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (+/- 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcute Covid Severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (78.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeurological Symptoms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of Smell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of Taste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiredness/Myalgias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeurology at TP-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of Smell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of Taste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiredness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eImaging was performed with a 3TMR imaging scanner (Ingenia; Philips Healthcare, Best, the Netherlands) with a 24-channel head coil. DCE perfusion imaging, 32 directional diffusion imaging and single voxel in addition to the routine clinical imaging (T-1 volumetric imaging and T-2 FSE) were performed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eT1-weighted imaging\u003c/b\u003e was performed with the following parameters 3DT-1 spoiled gradient recalled acquisition in steady state (SPGR): 128 sagittal slices, 1mm isotropic, time to repeat/time to echo (TR/ TE): shortest, field of view: 240, Matrix: 256/256.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiffusion tensor imaging (DTI)\u003c/b\u003e: The DTI protocol consisted of a single-shot spin-echo-based echo- planar diffusion-weighted imaging with three averages and 36 gradient encoding directions, with b values of 0 and 1,000 s/mm2. The imaging parameters were slice thickness 5mm, interslice gap 1.5mm, FOV: 230 X230, matrix 128/128, TR 3500 and TE 96Msec.\u003c/p\u003e \u003cp\u003eImage processing.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDCE perfusion MRI\u003c/strong\u003e \u003cp\u003eThe DCE-MRI sequence was obtained using 3D T1- weighted spoiled gradient echo sequence in the axial plane covering the entire brain [TR and TE1\u0026frasl;4shortest (Act TR/TE 15/3.0 ms, temporal resolution 5.8, flip angle 150, matrix\u0026thinsp;=\u0026thinsp;184 x 141, number of slices\u0026thinsp;=\u0026thinsp;23, slice thickness\u0026thinsp;=\u0026thinsp;4 mm, number of signal averages\u0026thinsp;=\u0026thinsp;1, temporal resolution\u0026thinsp;=\u0026thinsp;5.8/dynamic, number of dynamics\u0026thinsp;=\u0026thinsp;90 and scanning time\u0026thinsp;=\u0026thinsp;9.06 min. Contrast injection was commenced 6 s after the start of the dynamic MRI acquisitions, given in the form of a bolus injection of gadobutrol (Gadovist, Bayer, California, USA) at a concentration of 0.1 mmol/kg of body weight at 3 ml/s. Following the DCE-MRI scan, postcontrast- enhanced volumetric T1-weighted images were acquired as part of the routine clinical examination.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eImage Processing:\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eDTI and DTI-ALPS:\u003c/h2\u003e \u003cp\u003eThe DTI was processed using FSL toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). FSL's eddy correction tool was used for pre-processing the data. The brain extraction tool (BET) was used to create brain masks. Subsequently, water diffusivity along the x (Dx), y(Dy), and z (Dy) axes and fractional anisotropy (FA) maps were computed for each DTI scan. The tract-based skeleton statistic (TBSS) was used for the registration of FA maps from each participant onto an MNI FA atlas. All diffusivity maps were aligned to the same space using TBSS non-FA scripts. This method extracted affine matrices and warp fields derived from FA registration and applied them to these diffusivity maps.\u003c/p\u003e \u003cp\u003eTo calculate the DTI-ALPS, regions of interest (ROI) were defined as 3 mm x 3mm rectangle ROIs. The ROI were placed in the projection- and association-fiber regions in the horizontal plane of the lateral ventricle body. Three ROIs were placed in bilateral regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe DTI-ALPS is calculated as:\u003c/p\u003e \u003cp\u003eDTI-ALPS= (mean (Dx (proj),Dy (assoc)))/(mean (Dy(proj),Dz (assoc)) )\u003c/p\u003e \u003cp\u003eHere, Dx (proj) and Dy (assoc), Dz (assoc) are the mean diffusivity in the ROI placed in projection fibers and the association fibers along the x-axis, y-axis, and z-axis, respectively.\u003c/p\u003e \u003cp\u003eThe DTI-ALPS values were calculated for each patient and controls at baseline and patients at the longitudinal timepoint.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDCE perfusion MRI\u003c/strong\u003e \u003cp\u003eDCE studies were processed with nordicICE [nordicICE (NICE) 4.0.4; NordicNeuroLab, Bergen, Norway], a propriety software that includes brain extraction, motion correction and image registration. We assessed the DCE-derived metric K trans in multiple regions of the brain. The k trans images were interrogated by placing multiple regions of interest (ROI) in the following areas of the brain [basal ganglia (caudate and lentiform nucleus), frontal cortex, frontal white matter, thalami, splenium of corpus callosum, occipital cortex and white matter, internal capsule, brainstem and cerebellar lobes) by two radiologists, one with 25 years of experience and one with 3 year of experience (JC, AT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The volumes of the ROIs were 0.7ml and whenever the area was smaller due to volume loss, the ROI was adjusted to reduce the effects of CSF. K trans values were obtained from identical regions of the brain from the opposite hemispheres and the average values were taken to compare with the normal controls (e.g. average values of both hemispheres interrogated from individual anatomical regions).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eOut of 14 patients, only 10 patients had follow-up DTI study and hence in the final analysis we included those who had DTI study at both time points (n-10). Given the small size of the sample and nonnormality of data in the descriptive analyses, nonparametric analyses were used for group comparisons. A two-sample Wilcoxon rank sum test was used to show the differences in DTI-ALPS between the patients and controls in the predefined ROIS. Paired Wilcoxon rank sum test was used to compare the DTI-ALPS values between two time points and within the regions of interest (Both hemispheres). Spearman\u0026rsquo;s correlation coefficient (rho) was used to assess the correlation between blood brain barrier measure (K-trans) and DTI-ALPS scores in the regions where there was significant difference between group.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDTI-ALPS:\u003c/h2\u003e \u003cp\u003eThere was a significant difference (z\u0026thinsp;=\u0026thinsp;2.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) in DTI-ALPS between patients and controls in the left hemisphere (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). However, there was no statistically significant change (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) over time within patients in this ROI. There was a significant difference in DTI-ALPS between Left and Right ROI within patients (z\u0026thinsp;=\u0026thinsp;2.5, p\u0026thinsp;=\u0026thinsp;0.01)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation with K trans and Neurocognitive scores:\u003c/h2\u003e \u003cp\u003eExploratory correlations were performed between K-Trans, ALPS index and neurocognitive scores. There was no correlation of NCS with ALPS score. Mean K trans scores did not correlate with ALPS score. However central white matter K trans has shown positive correlation on the side of abnormal ALPS (rho\u0026thinsp;=\u0026thinsp;0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.03) (Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this proof-of-concept prospective case-control study, we investigated glymphatic system abnormalities, as expressed by the DTI-ALPS index, and correlated them with capillary permeability and neurocognitive scores in individuals affected by neurocognitive impairment due to COVID-19. Our findings revealed decrease in the DTI-ALPS index in the left hemisphere, while the ALPS index in the right hemisphere remained within normal limits. We observed a strong negative correlation between the DTI-ALPS index and K trans in the central white matter, but no statistically significant correlation was found between the ALPS index and neurocognitive scores.\u003c/p\u003e \u003cp\u003eWe believe our study is the first to delve into the relationship between DTI-ALPS and the BBB metric K trans. Our own prior research has revealed disruptions in the BBB, likely triggered by glutamatergic excitotoxicity, and subsequent changes in white matter integrity among patients with long COVID\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e. The BBB disruption, a hallmark of neuroinflammation, is a known characteristic of several neurodegenerative disorders, often occurring alongside abnormalities in the glymphatic system, a crucial paravascular drainage pathway in the brain.\u003c/p\u003e \u003cp\u003eWu et al in their study has shown variable ALPS changes in long COVID-19 subjects and concluded that it is likely secondary to neuronal inflammation and secondary glymphatic dysfunction\u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e. The glymphatic system functions to expel metabolic waste from the brain's interstitium via paravascular spaces, either into the dural sinuses or through perineural spaces from the basal foramen into the cervical lymphatics during REM sleep. Anosmia, a principal presentation of COVID-19, and the volume loss in the brain regions associated with smell, and memory indicate that the influx and efflux mechanisms in the perineural space of the olfactory bulbs is damaged and a resultant abnormalities in glymphatic drainage from the olfactory-gustatory circuit\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral earlier studies have identified excitotoxicity as a primary pathological mechanism contributing to neurocognitive impairment in long COVID-19 \u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e. The excitotoxicity, in addition to causing blood-brain barrier (BBB) disruption, also is known to impair aquaporin-4 channels\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e. This dysregulation of water channels has been shown to result in impaired cerebrospinal fluid (CSF) influx and disruption of the CSF-interstitial fluid (ISF) turnover pathway\u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e and thus appear to be having a synergistic relation with BBB disruption.\u003c/p\u003e \u003cp\u003eDiffusion tensor imaging metrics from projection and association fibers at the mid-body level of lateral ventricles, where medullary veins run perpendicular to ventricular bodies, have been used to measure paravascular flow in the interstitium. This metric, expressed as the ALPS Index, is a non-invasive method to measure glymphatic drainage abnormalities and has been shown to be abnormal in several neurological disorders associated with neuroinflammation.\u003c/p\u003e \u003cp\u003eOur study revealed an asymmetric reduction in the ALPS index in the left hemisphere compared to the right which is not surprising given that most studies in the literature measure the ALPS index from the dominant hemisphere, where diffusion metrics differ from the non-dominant hemisphere\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e. Wu et al study has shown similar observations with asymmetrical glymphatic dysfunction between hemispheres\u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e. Furthermore, this heterogeneity in the involvement could be secondary to non-uniform neuroinflammation. Our earlier longitudinal study in patients with long COVID also showed asymmetrical diffusion changes between hemispheres, indicating variable neuroaxonal injury across the brain\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, we also found that Dx- values in COVID-19 were mildly decreased compared to controls, leading to a decrease in the DTI-ALPS index (trending significance with variance between the controls and patients is 30.3% on polynomial regression). This suggests that diffusion is hindered more significantly in projection fibers than in association fibers, particularly in the plane perpendicular to the ventricles, possibly due to increased extracellular water content and changes in white matter fiber structure. The lack of significant change in the ALPS index over 12 months suggests that alterations contributing to the pathogenesis of altered drainage may take longer to revert or may be irreversible, a finding also reflected in the lack of statistically significant change in K trans values.\u003c/p\u003e \u003cp\u003eDespite the valuable insights provided by our study, several limitations exist, primarily the small sample size, partially compensated by the longitudinal design. Additionally, not all subjects underwent a full battery of neurocognitive assessments. Future studies with larger sample sizes and longitudinal designs are warranted to further elucidate these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study suggests that neuroinflammation and disordered drainage may contribute to neuroaxonal injury in long COVID-19, and DTI-ALPS could serve as a powerful non-invasive biomarker to identify these abnormalities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePASC: Post-acute sequelae of SARS-CoV-2 infection, DTI-ALPS: Diffusion along the perivascular spaces DTI (DTI-ALPS), NCI: Neuro cognitive impairment, BBB: Blood brain barrier.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eLocal ethics approval was obtained. (2022/ETH0022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors did not receive any funding for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have obtained part of the cohort of patient\u0026rsquo;s data from the ADAPT Study (PI: GM: [email protected]).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJoga Chaganti\u003c/strong\u003e: Concept and Major contributor in writing article and processing of the DTI data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTanush Talekar\u003c/strong\u003e: \u0026nbsp;DTI evaluation and processing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBruce Brew\u003c/strong\u003e: Concept and Major contributor in writing the article and neurocognitive testing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChaganti J, Poudel G, Cysique LA, Dore GJ, Kelleher A, Matthews G, et al. Blood brain barrier disruption and glutamatergic excitotoxicity in post-acute sequelae of SARS COV-2 infection cognitive impairment: potential biomarkers and a window into pathogenesis. Frontiers in Neurology. 2024;15:1350848.\u003c/li\u003e\n\u003cli\u003eLiang T, Chang F, Huang Z, Peng D, Zhou X, Liu W. Evaluation of glymphatic system activity by diffusion tensor image analysis along the perivascular space (DTI-ALPS) in dementia patients. The British journal of radiology. 2023;96(1146):20220315.\u003c/li\u003e\n\u003cli\u003eTaoka T, Masutani Y, Kawai H, Nakane T, Matsuoka K, Yasuno F, et al. Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer\u0026rsquo;s disease cases. Japanese journal of radiology. 2017;35:172-8.\u003c/li\u003e\n\u003cli\u003eAlbayram MS, Smith G, Tufan F, Tuna IS, Bostancıklıoğlu M, Zile M, et al. Non-invasive MR imaging of human brain lymphatic networks with connections to cervical lymph nodes. Nature communications. 2022;13(1):203.\u003c/li\u003e\n\u003cli\u003eThakkar RN, Kioutchoukova IP, Griffin I, Foster DT, Sharma P, Valero EM, et al. Mapping the Glymphatic pathway using imaging advances. J. 2023;6(3):477-91.\u003c/li\u003e\n\u003cli\u003eSteinruecke M, Tiefenbach J, Park JJ, Kaliaperumal C. Role of the glymphatic system in idiopathic intracranial hypertension. Clinical Neurology and Neurosurgery. 2022;222:107446.\u003c/li\u003e\n\u003cli\u003eRingstad G, Vatnehol SAS, Eide PK. Glymphatic MRI in idiopathic normal pressure hydrocephalus. Brain. 2017;140(10):2691-705.\u003c/li\u003e\n\u003cli\u003eYang G, Deng N, Liu Y, Gu Y, Yao X. Evaluation of glymphatic system using diffusion MR technique in T2DM cases. Frontiers in human neuroscience. 2020;14:300.\u003c/li\u003e\n\u003cli\u003eKamagata K, Andica C, Takabayashi K, Saito Y, Taoka T, Nozaki H, et al. Association of MRI indices of glymphatic system with amyloid deposition and cognition in mild cognitive impairment and Alzheimer disease. Neurology. 2022;99(24):e2648-e60.\u003c/li\u003e\n\u003cli\u003eWu CH, Chang FC, Wang YF, Lirng JF, Wu HM, Pan LLH, et al. Impaired glymphatic and meningeal lymphatic functions in patients with chronic migraine. Annals of Neurology. 2024.\u003c/li\u003e\n\u003cli\u003eCysique LA, Jakabek D, Bracken SG, Allen‐Davidian Y, Heng B, Chow S, et al. The kynurenine pathway relates to post‐acute COVID‐19 objective cognitive impairment and PASC. Annals of Clinical and Translational Neurology. 2023;10(8):1338-52.\u003c/li\u003e\n\u003cli\u003eWu L, Zhang Z, Liang X, Wang Y, Cao Y, Li M, et al. Glymphatic system dysfunction in recovered patients with mild COVID-19: A DTI-ALPS study. Iscience. 2024;27(1).\u003c/li\u003e\n\u003cli\u003eGreene C, Connolly R, Brennan D, Laffan A, O\u0026rsquo;Keeffe E, Zaporojan L, et al. Blood\u0026ndash;brain barrier disruption and sustained systemic inflammation in individuals with long COVID-associated cognitive impairment. Nature Neuroscience. 2024;27(3):421-32.\u003c/li\u003e\n\u003cli\u003eManganotti P, Michelutti M, Furlanis G, Deodato M, Stella AB. Deficient GABABergic and glutamatergic excitability in the motor cortex of patients with long-COVID and cognitive impairment. Clinical Neurophysiology. 2023;151:83-91.\u003c/li\u003e\n\u003cli\u003eIliff JJ, Wang M, Zeppenfeld DM, Venkataraman A, Plog BA, Liao Y, et al. Cerebral arterial pulsation drives paravascular CSF\u0026ndash;interstitial fluid exchange in the murine brain. Journal of Neuroscience. 2013;33(46):18190-9.\u003c/li\u003e\n\u003cli\u003eTaoka T, Naganawa S. Neurofluid dynamics and the glymphatic system: a neuroimaging perspective. Korean Journal of Radiology. 2020;21(11):1199.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4551571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4551571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Purpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe glymphatic system, a waste clearance pathway, has been implicated in several neurological conditions associated with neuroinflammation. COVID-19 associated neurocognitive impairment, part of the post-acute sequelae of SARS-CoV-2 infection (PASC), is strongly associated with neuroinflammation and disrupted blood-brain barrier (BBB). Several studies have implicated a synergistic interaction between the glymphatic system dysfunction and BBB disruption. In this proof-of-concept study, we investigated the relationship of the diffusion along the perivascular spaces DTI (DTI-ALPS) and increased capillary permeability metric- K trans derived from DCE perfusion in patients with PASC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e14 subjects with PASC who had persisting symptoms of anosmia, ageusia, fatigue, and cognitive impairment (CI) and ten healthy age and sex matched controls were recruited. All PASC subjects underwent routine and advanced MR imaging early at two time points, (3 months +/- 2 weeks) referred as Time Point 1 (TP-1) and 10 repeated the MRI scan 12 months (+/- 2 weeks) after referred as Time Point 2 (TP-2), while the controls had MR imaging done only at TP-1. All had elaborate neurocognitive assessment. In the final analysis we included those who had DTI study at both time points (n-10). MR imaging included DCE perfusion and DTI in addition to anatomical imaging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the small size of the sample and nonnormality of data in the descriptive analyses, nonparametric analyses were used for group comparisons. A two-sample Wilcoxon rank sum test was used to show the differences in DTI-ALPS between the patients and controls in the predefined ROI. Spearman’s correlation coefficient (rho) was used to assess the correlation between K-trans and DTI-ALPS index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was significant reduction in DTI-ALPS index between the patients and controls in the left hemisphere (z=2.04, \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.04). However, there was no significant change over time in the index. There was a strong inverse correlation between the central white matter K trans and DTI-ALPS index (rho=0.66, p\u0026lt; 0.03).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study suggests that BBB disruption and disordered glymphatic drainage may contribute to neuroaxonal injury in patients with PASC, and DTI-ALPS index could serve as a powerful non-invasive biomarker.\u003c/p\u003e","manuscriptTitle":"Asymmetrical glymphatic dysfunction in patients with Long Covid associated neurocognitive impairment- Correlation with BBB disruption","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 14:52:28","doi":"10.21203/rs.3.rs-4551571/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-09T10:21:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-06T17:45:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220269593783471881386161585596705298956","date":"2024-08-19T13:52:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-05T14:50:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109786984412550085063334312490167255369","date":"2024-06-21T13:54:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287961520617345461596108795502158681778","date":"2024-06-21T08:41:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-16T08:01:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-12T05:13:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-12T01:00:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2024-06-08T18:23:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20aaa10c-ab19-4ef7-adeb-4070b6a2c0fd","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T15:58:47+00:00","versionOfRecord":{"articleIdentity":"rs-4551571","link":"https://doi.org/10.1186/s12883-025-04133-4","journal":{"identity":"bmc-neurology","isVorOnly":false,"title":"BMC Neurology"},"publishedOn":"2025-03-19 15:56:53","publishedOnDateReadable":"March 19th, 2025"},"versionCreatedAt":"2024-07-02 14:52:28","video":"","vorDoi":"10.1186/s12883-025-04133-4","vorDoiUrl":"https://doi.org/10.1186/s12883-025-04133-4","workflowStages":[]},"version":"v1","identity":"rs-4551571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4551571","identity":"rs-4551571","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}